Method and apparatus for determining unknown magnetic ink characters

ABSTRACT

An apparatus and method for processing documents is provided. Preferably the apparatus includes a plurality of document processing features operable for processing a variety of documents, such as incoming mail. The apparatus may include a feeder for feeding the documents from a sack of documents, an extraction station for extracting documents from envelopes, an optical scanner for scanning optical images of the documents, a orientation detection station for determining the orientation of the documents, a MICR station for identifying unknown magnetic ink markings and a sorting station for sorting the documents. The documents include a document having an unknown magnetic marking that is magnetized and then scanned to obtain a plurality of data points indicative of the unknown marking. Using cross correlation, the data for the unknown marking is then compared with a series of models corresponding to a series of known magnetic markings. The unknown marking is determined to be the marking corresponding to the model that most closely correlates to the unknown marking.

FIELD OF THE INVENTION

The present invention relates generally to the field of documentprocessing and specifically to identifying characters printed inmagnetic ink. More specifically, the present invention relates to thefield of identifying MICR characters printed on documents, such aschecks.

BACKGROUND OF THE INVENTION

In the field of document processing, characters are sometimes printed inmagnetic ink. One such example is the printing of MICR characters on thebottom of checks. When processing documents it is often desirable toextract information from the documents so that the information can beretrieved during subsequent processing of the documents. Therefore, incertain applications it is desirable to read the characters that areprinted in magnetic ink when processing the documents.

One of the shortcomings of the known methods of reading magnetic inkcharacters is that the read rates are not high enough for certainapplications. For instance, although the typical read rates are quitehigh (on the order of 95%), some applications require read rates as highas 99% or higher.

SUMMARY OF THE INVENTION

Accordingly, the present invention provides an improved method andapparatus that address the shortcomings of the known methods for readingmagnetic ink characters. In one aspect, the present invention provides amethod wherein an unknown character on a document printed with magneticink is magnetized. The magnetized ink is then scanned by a read head toprovide a set of data corresponding to the unknown character. Usingcross correlation, the data for the unknown character is compared to aseries of data sets that correspond to known characters. The unknowncharacter is then determined to be the character corresponding to theknown data set that most closely correlates to the data for the unknowncharacter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary and the following detailed description of thepreferred embodiments of the present invention will be best understoodwhen read in conjunction with the appended drawings, in which:

FIG. 1 is a diagrammatic illustration of an apparatus according to thepresent invention;

FIG. 2 is a diagrammatic view of a MICR station of the apparatusillustrated in FIG. 1;

FIG. 3 is a flow chart illustrating a process of identifying magneticink markings;

FIG. 4 is a chart illustrating MICR waveform correlation versustransport speed; and

FIG. 5 is a series of chart illustrating waveforms produced by variousMICR characters.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the figures, wherein like elements are numbered alikethroughout, an apparatus for processing documents is designatedgenerally 10. The device 10 preferably includes numerous features forprocessing documents, such as mail, including such features as an inputbin 20 for receiving a stack of mail, a feeder 25 for serially feedingthe envelopes into a transport path 15, a cutter 30 for cutting open theenvelopes, and an extraction station 35 for extracting the documentsfrom the envelopes. Preferably, the device 10 further includes aseparation station 40 for separating packets of documents from anenvelope so that the documents in an envelope are then serially fedalong the transport. In addition, the device preferably includes animaging station 50 for acquiring images of the documents, and anorientation station 45 that is operable to reorient the documents into adesired orientation by selectively flipping, twisting, and/or reversingthe documents. Furthermore, the device includes a MICR station 60 fordetecting and reading magnetic markings on a document.

Although, all of the above referenced features are not necessary for theimplementation of the present invention, the present invention ispreferably incorporated into an automated document processing machine,such as the apparatus disclosed in U.S. Pat. No. 5,842,577 or theapparatus disclosed in U.S. Pat. No. 5,460,273, each of which are herebyincorporated herein by reference.

Referring now to FIG. 2, the details of the MICR station will bedescribed in greater detail. The MICR station 60 includes one or moremagnetizing or charge heads 62, and one or more read heads 64 locateddownstream from the charge heads. The charge heads 62 comprise a magnet,such as a permanent magnet or an electromagnet that provides a magneticfield. The read heads are operable to convert a magnetic field into anelectrical signal. For instance, preferably the read heads include acoil so that when exposed to a magnetic field, the read heads provide anelectrical signal indicative of the change in the strength of themagnetic field. The MICR station 60 either includes or is connected to aMICR processor 70 that receives signals from the read heads 64 andprocesses the signals to determine the identity of the characters, asdiscussed further below. In the present instance the MICR processor is adigital signal processor. However, the MICR processor may be in the formof a different type processor, such as a microprocessor.

Preferably, the MICR station is positioned along the transport path sothat the transport path conveys the documents past the charge heads 62and then the read heads 64. To improve the MICR detection, preferably aflexible nonferrous metallic band that urges the documents into contactwith each charge heads 62 and read heads 64 as the documents areconveyed along the transport path.

Preferably, the documents are properly oriented before they are fed intothe MICR station. Specifically, preferably the documents are oriented sothat the documents enter the MICR station lead edge first and front facetoward the charge heads 62 and read heads 64. Accordingly, preferablythe documents are either oriented into the proper orientation by aseparate operation (either manually or automatically) before processingby the device 10 or the device preferably includes an orientationstation for automatically detecting the orientation and selectivelyreorienting the documents as discussed above.

If the documents are properly oriented, the MICR station 60 can beconfigured with a single charge head 62 and a single read head 64positioned so that the charge head and read head engage a lower portionof the documents. Specifically, preferably the transport path 15comprises a pair of opposing belts and the documents are conveyedbetween the opposing belts. On many documents, such as checks, the MICRline is located on the bottom portion of the document. The belts engagethe documents along a middle portion of the documents so that the topportion of the documents project above the belts and the bottom portionprojects below the belts. If the documents are properly oriented beforeentering the MICR station, a single charge head 62 and a single readhead 64 can both be positioned below the height of the belts to engagethe lower portion of the documents.

Although the documents are preferably reoriented before entering theMICR station, the MICR station can be configured to accommodatemis-oriented documents. Specifically, by employing two charge heads andtwo read heads, the charge heads and read heads can be positioned bothabove and below the level of the belts that convey the documents. Inthis way, the charge heads and read heads can operate on the documentsregardless of the orientation of the documents and regardless of thelocation of the MICR line on the documents. In addition, since the MICRstation can operate on the MICR markings through the paper of thedocument, the MICR station can operate on the documents regardless ofwhether the front or back face of the document faces the charge headsand the read heads. However, if the document is not properly oriented,the analysis of the data from the read heads may need to be manipulatedor analyzed differently, as discussed further below.

As a document passes through the MICR station 60, the charge headimparts a magnetic charge onto the magnetic ink of the marking on thedocument. The document is then conveyed past the read heads 64. Sincethe magnetic ink is magnetized, the magnetic ink provides a magneticfield that is detected by the read head. More specifically, the readheads 64 detect the magnetic field and produce a voltage signalproportionate to the change in strength of the detected magnetic field.In this way, the voltage signal produced by the read heads 64 varies asthe magnetic field strength of the magnetic ink marking varies when itis conveyed past the read head. In other words, if the magnetic inkmarking is a straight line of uniform thickness, the magnetic fieldstrength would (ideally) be a continuous magnetic field of constantstrength as the magnetic marking passes the read head. Accordingly, theread head would produce a voltage signal having an initial spike up to apeak as the beginning of the line passes the read head, and then adownward slope down to a flat line of zero volts as the line continuespast the read head (i.e. since the line is continuous strength, thechange in magnetic field strength is zero).

MICR characters are designed to produce an output signal that isindicative of the corresponding character. For example, MICR charactersprinted according to the E-13B standard produce a waveform that haspeaks and troughs that are unique to each character. In other words, asthe MICR character passes the read head, the character is printed sothat the magnetic field strength should increase and decrease accordingto a particular wave form that is indicative of the particularcharacter. The E-13B MICR characters and the corresponding waveforms areshown in FIG. 5.

Since the waveform for each MICR character is unique, an unknown MICRcharacter can be determined by comparing its waveform with the knownMICR character waveforms. Accordingly, as a document passes the readhead 64, the read head provides an output signal indicative of thechange in magnetic field strength. Specifically, the read head providesa signal of varying strength to the MICR processor 70 as a character isconveyed past the read head. The MICR processor 70 then samples thesignal at a plurality of points to provide a plurality of data points(preferably 128) indicative of the signal received from the read head64. The data points are then stored in the MICR processor 70 for furtherprocessing and analysis.

Since MICR markings are typically printed as a series of marking, theMICR processor separates the data so that the data for one character isseparated from the data for the previous character and the data for eachsubsequent character. In addition, since it is desirable to identify theseries of MICR markings for a particular document, the MICR processoridentifies the beginning and end of the series of MICR characters andassociates the MICR character series with the corresponding documentafter the MICR characters are identified. Accordingly, preferably theMICR processor is operable to identify the series of MICR characters andexport the information so that the MICR information can be stored in adata file for the corresponding document.

The MICR processor 70 analyzes the data for an unknown magnetic markingthat is scanned by the MICR station by comparing the data for theunknown marking with samples or models that correspond to known MICRmarkings. The MICR processor determines which model most closely matchesthe data from the unknown marking and then identifies the unknownmarking as the MICR marking corresponding to such model. The MICRprocessor repeats this analysis for each separate marking identified ona document.

The MICR processor compares the data for an unknown marking with themodels for the known MICR characters using cross correlation.Specifically, the model for each known character comprises a series ofdata points indicative of the character. For instance, in the case ofMICR font E13B, the data would correspond to a series of data pointsthat would form a curve in the appropriate shape illustrated in FIG. 5.

The cross correlation analysis performed by the MICR processor 70 isperformed according to the following analysis. Each data point in thedata set for the unknown marking is multiplied by the corresponding datapoint in the data set for the first model. These products are thensummed to provide a cross correlation value. For instance, the value ofthe first data point in the unknown marking set is multiplied by thevalue of the first data point in the first model. In the preferredexample using 128 samplings, this would result in a series of 128products, which would then be summed together to result in a correlationvalue.

The timing of the beginning of the data set for the unknown marking isimportant in ensuring that it correlates correctly with the appropriatemodel. For instance, if the waveform for the unknown marking is the sameshape as the waveform for the number zero, but the waveform is shiftedover to the right (a shift in time, since the waveform is a function ofvoltage versus time), the correlation analysis will not result in aclose correlation even though the shape of the waveforms is similar. Toaccount for this, the cross correlation is repeated a number of timeswhile shifting the data points to account for the possible shift in thedata (i.e. a shift in the waveform).

To accomplish the shift, the data for each of the models is shifted sothat the second data point is considered to be the first data point, thethird is considered to be the second, and so on. In other words, thesecond data point is multiplied against the first data point in theunknown data set, and so on. Preferably, the first data point is thenwrapped around so that it is analyzed as the last data point in the set.This shifted data set results in a second correlation value for theunknown marking. The data may be shifted any number of times up to thetotal number of data points. However, preferably the data is shifted 24times to produce 24 correlation values for the correlation between eachmodel and the unknown character. Alternatively, rather than shifting thedata for each of the models, the data for the unknown character can beshifted similarly to the shifting described above to attempt to alignthe data sets.

These 24 correlation values are then analyzed so that the greatest ofthe 24 correlation values is identified as the correlation value betweenthe unknown character and the first model. This process is then repeatedfor each model so that the data for the unknown character is comparedagainst each model 24 times. In other words, for each model, 24correlation values are determined and each correlation value is based onthe summation of 128 numbers (i.e. the product of the values of thecorresponding 128 data points). In short the number of computations foreach character is equal to the product of the number of data points, thenumber of shifts and the number of models to which the data is compared.For this reason, it is desirable to limit the analysis to a single typeof MICR character to limit the number of models, thereby limiting theamount of computation necessary for each character.

The above cross correlation value can be summarized by the followingequation:

$r_{i} = {\sum\limits_{k = 0}^{N - 1}{X_{k}*Y_{{mod}{({{i + k},N})}}}}$wherein x is the unknown waveform, y is one of the models, N is thetotal number of samples (e.g., 128), and i is incremented from zero tothe number of shifts desired (e.g., 24). In addition, mod refers to amodulo operator. Mod (AB) gives the remainder from the division of A/B.

As discussed above, the cross correlation of the data for an unknowncharacter will result in a number of cross correlation values (one valuefor each model). The process may operate by simply assuming that theunknown character is the character corresponding to whichever modelresults in the highest correlation value. However, it is possible thatthe unknown marking is a stray marking or is an improperly printedmarking. In such an instance, there will be a maximum correlation value,but the maximum will be lower than expected for a marking that actuallycorrelates to an expected marking. Accordingly, if the maximumcorrelation value is below a threshold, the marking is not identified asone of the known characters. The marking is then either identified as anunknown marking or the data for the unknown marking is further analyzedas discussed below.

Various factors can affect the correlation between the data for theunknown character and the data for the models, leading to an impropercorrelation. One such factor is the amplitude of the data for theunknown marking. Variations in the printed magnetic marking can affectthe amplitude. For instance, the magnetic ink is made magnetizable byferrous particles that are mixed in with the ink. If the ratio offerrous particles to ink is more or less than the anticipated amount,the magnetic field of the ink will be greater or less than expected.Similarly, if the ink is printed more lightly than expected (such aswhen an ink cartridge is running low), the magnetic field of the markingwill be less than expected.

To compensate for the potential variations in amplitude, preferably thedata is normalized. Specifically, preferably each correlation valuedetermined for each model is normalized by dividing the correlationvalue by a normalization factor. Once normalized, the correlation valuewill range from zero (meaning no correlation) to one (meaning perfectcorrelation). The normalization factor is determined by summing thesquared value of each data point for the unknown marking and multiplyingthis sum by the sum of the squared value of each data point for therelevant model. The square root of this product is then taken to providethe normalization value. In other words, the normalization factor can besummarized according to the following equation:

${{normalization}\mspace{14mu}{factor}} = \sqrt{\left( {\sum\limits_{k = 0}^{N - 1}X_{k}^{2}} \right)\left( {\sum\limits_{k = 0}^{N - 1}Y_{k}^{2}} \right)}$

Another factor affecting the analysis of the data for the unknownmarking can be an upward or downward offset of the data. To compensatefor this offset, preferably, the data for the unknown marking and thedata for the models can be offset by a factor equal to the mean valuefor the data set. In other words each data point in a data set isshifted by a factor equal to the sum of the data points divided by thenumber of data points. In other words,

$X_{k} = {{X_{k} - {\frac{\sum\limits_{k = 0}^{N - 1}X_{k}}{N}\mspace{14mu}{and}\mspace{14mu} Y_{k}}} = {Y_{k} - \frac{\sum\limits_{k = 0}^{N - 1}Y_{k}}{N}}}$

Yet another factor that can affect the correlation is the transportspeed. If the transport speed is faster than expected, the waveform forthe unknown marking would be compressed, whereas if the transport speedis slower than expected the waveform would be elongated. It has beendetermined that a variation in track speed by as little as approximately6 percent can sufficiently affect the correlation to lead to either anincorrect identification of the character or a low enough correlationthat the character is rejected. The comparison of MICR waveformcorrelation and transport speed is illustrated graphically in FIG. 4. Tocompensate for the potential variation in transport speed, preferably aseries of alternate models is provided for instances in which thetransport speed is 3 percent faster or more, and a second alternateseries of models is provided for instances in which the transport speedis three percent slower or more.

The alternate series of models are utilized as follows. The time that ittakes the unknown character to pass by the MICR reader 64 can bemeasured by the MICR station. If the time is shorter than the time thatit should take a MICR character to pass by the read heads 64, the MICRprocessor 70 assumes that the transport speed is too fast, and theseries of models corresponding to a fast transport speed are utilizedfor the cross correlation analysis. Similarly, if the time is longerthan the appropriate time for a MICR character to pass the read heads64, then it is assumed that the transport speed is too slow and theseries of models corresponding to a slow transport are utilized for thecross correlation analysis.

Additionally, noise and other variables can affect the output signalfrom the read heads, causing the signal to be foreshortened or stretchedrelative to the actual length of the signal. As a result, the wrongseries of models may be utilized for the cross correlation analysis. Ifthe wrong series of models is used, the maximum correlation value willnot be as great as it would be if the proper series of models is used.Accordingly, if the maximum correlation value is below a pre-determinedthreshold, then the analysis for the data of the unknown character isalso performed using one of the other two series of models.Specifically, the duration of the signal from the read head is analyzedto determine which of the two other sets of models should be used. Theset of models that most closely relates to the duration of the signal isused. For instance, if the signal duration is two percent longer thanexpected, the nominal speed models are used for the cross correlation.However, if the correlation value using these models is not above athreshold, the analysis is performed using the models corresponding to aslow transport speed, since those models more closely correspond tosignal duration (i.e. 2% slow is closer to 3% slow than to 3% fast). Themaximum correlation value for all of the models used is then utilized toidentify the unknown character. It should be noted however, that if thesignal duration is unusually high or low and the correlation value isbelow the secondary threshold, then a second analysis using a second setof models is not performed, because it is assumed that the correlationwill not improve.

As described above, two thresholds may be utilized. An absolutethreshold for determining whether there is sufficient correlation toidentify the unknown character, and a secondary threshold fordetermining whether the alternate series of models should be used. Inthe present instance, the absolute threshold is approximately 0.76 andthe secondary threshold is approximately 0.86.

Yet another factor that affects the analysis of the characterrecognition is the orientation of the documents. As discussed above,preferably the documents are properly oriented. If a document ismis-oriented, the data from the read head 64 will not properly correlatewith the models. For instance, if the document enters the MICR station60 backwards (i.e. trailing edge first), the detected data will bebackwards relative to the models. Therefore, if the correlation for anunknown character is below a threshold, rather than rejecting thecharacter as being unable to be identified, the data may be manipulatedto account for the possibility that the document was mis-oriented. Thecorrelation analysis can then be performed on the manipulated data.

More specifically, the data is manipulated by reversing the data (i.e.re-ordering the data). The first data point is re-ordered as the lastdata point, the second data point is re-ordered as the second-to-lastdata point, and so forth. The re-ordered data is then analyzed tocorrelate the data with the character models.

As can be appreciated from the foregoing, if the orientation of thedocuments is random, the computation to evaluate each document can bedoubled. Accordingly, if the orientation of the documents is random, itmay be desirable to determine the orientation of the document beforeattempting to analyze the unknown marking or markings on the document.The scanned data for the documents can then be manipulated as necessaryto account for the orientation of the document before analyzing the datato identify the unknown marking.

The orientation of the documents can be determined according to one of avariety of ways. For instance, if the location of the unknown marking isexpected at a particular location on the document, then the orientationof the document can be determined based upon the distance the marking islocated from either the leading edge or the trailing edge. For example,for identifying the location of characters in a MICR line on a check,the MICR line is typically located in the lower left hand corner of acheck, and the length of a MICR line on a check falls within apre-determined range. Therefore, the orientation of a check can bedetermined by the distance from the leading edge of the document to thefirst detected magnetic ink marking. If the distance is below athreshold, then the check entered the MICR station with the left edgeleading. If the distance is above a threshold, then the check enteredthe MICR station with the right edge leading.

In the foregoing discussion, the analysis is described in connectionwith E13B MICR characters. However, the methodology is also particularlysuited to identify other types of MICR characters, such as CMC-7 typeMICR character, which are characters formed by a series of spaced apartbars. The length of the bars vary as well as the spacing between thebars that form each character. Based on these characteristics a seriesof models can be created that correspond to each of the CMC-7characters.

Similarly, the discussion describes a particular type of crosscorrelation that is suitable for use. However, it will be recognizedthat variations on the cross correlation analysis can be made and stillbe considered cross correlation as that term is used herein. Forinstance, in certain instances it may be desirable to use crosscorrelation implemented by using a Fast Fourier Transform approach. Suchan approach is still a cross correlation analysis as that term is usedherein. More specifically, the term cross correlation as used herein ismeant to encompass any correlation analysis that measures the similaritybetween two different data sets computed by the sum of the crossproducts between the two data sets, and particularly the cross productat different lags.

These and other advantages of the present invention will be apparent tothose skilled in the art from the foregoing specification. Accordingly,it will be recognized by those skilled in the art that changes ormodifications may be made to the above-described embodiments withoutdeparting from the broad inventive concepts of the invention. It shouldtherefore be understood that this invention is not limited to theparticular embodiments described herein, but is intended to include allchanges and modifications that are within the scope and spirit of theinvention as set forth in the claims.

1. A method for identifying an unknown MICR character on a document,comprising the steps of: providing a document within an envelope,wherein the document has an unknown MICR character printed with magneticink; extracting the document from the envelope; imparting a magneticcharge onto the magnetic ink to magnetize the unknown character;scanning the unknown character with a magnetic read head to obtain a setof data indicative of the unknown character; providing a plurality ofpredetermined data sets wherein each data set corresponds to aparticular MICR character; determining a correlation value of thesimilarity between each of the predetermined data sets and the data setfor the unknown character using cross correlation; determining anormalization value for the cross correlation of each data set and thedata set of the unknown character and dividing the correlation value bythe normalization value; identifying the maximum correlation value; andidentifying the unknown character as the MICR character corresponding tothe data set having the maximum correlation value.
 2. The method ofclaim 1 wherein the step of determining a correlation value using crosscorrelation comprises the step of determining the sum of the crossproduct between each of the predetermined data sets and the data set forthe unknown character.
 3. The method of claim 1 comprising the steps of:incrementally shifting the data to re-order the data for the unknownMICR character to provide a plurality of re-ordered data sets for theunknown MICR character; determining a correlation value of thesimilarity between each of the predetermined data sets and each of there-ordered data sets for the unknown character using cross correlation.4. The method of claim 1 comprising the steps of: incrementally shiftingthe data to re-order the data for each of the predetermined data sets toprovide a plurality of re-ordered data sets for each of thepredetermined data sets; determining a correlation value of thesimilarity between each of the re-ordered predetermined data sets andthe data set for the unknown character using cross correlation.
 5. Themethod of claim 1 comprising the steps of determining an offset valuefor each data set based upon the corresponding mean value of each dataset, and offsetting each data point in each data set by thecorresponding offset value.
 6. The method of claim 1 wherein the step ofscanning comprises conveying the document past the magnetic read headand measuring the magnetic field strength as the document passes theread head.
 7. The method of claim 1 comprises the step of an alternativecomparison, comprising the steps of: providing a plurality of alternatedata sets corresponding to the MICR characters; determining acorrelation value of the similarity between each of the alternate datasets and the data set for the unknown character using cross correlation;identifying the maximum correlation value of both the predetermined datasets and the alternate data sets; and identifying the unknown characteras the MICR character corresponding to the data set having the maximumcorrelation value.
 8. The method of claim 7 comprising the step ofdetermining whether the maximum correlation value exceeds a thresholdand the alternate comparison is performed if the correlation value doesnot exceed the threshold.
 9. The method of claim 7 wherein the methodcomprises determining the length of time that it takes the character topass the read heads, and the alternate comparison is performed if thelength of time is above a time threshold.
 10. The method of claim 7wherein the method comprises determining the length of time that ittakes the character to pass the read heads, and the alternate comparisonis performed if the length of time is below a time threshold.
 11. Themethod of claim 1 comprising the steps of scanning the document toobtain a set of optical image data corresponding to the document, andexporting the optical image data and data regarding the identified MICRcharacter to a data file for the document.
 12. The method of claim 1comprising the steps of determining the orientation of the document andselectively re-orienting the document.
 13. The method of claim 12comprising the step of sorting the document in response to a detectedcharacteristic of the document.
 14. The method of claim 1 wherein eachcross correlation in the step of determining a correlation valuecomprises cross correlating the data set for the unknown character andone of the predetermined data sets by determining the product of eachdata point in the data set for the unknown character with thecorresponding data point in the one predetermined data set and summingthe products.
 15. A method for identifying an unknown MICR character ona document, comprising the steps of: providing a document having anunknown MICR character printed with magnetic ink; imparting a magneticcharge onto the magnetic ink to magnetize the unknown character;scanning the unknown character with a magnetic read head to obtain a setof data indicative of the unknown character; providing a plurality ofpredetermined data sets wherein each data set corresponds to aparticular MICR character; determining an offset value for each data setbased upon the corresponding mean value of each data set, and offsettingeach data point in each data set by the corresponding offset valuedetermining a correlation value of the similarity between each of thepredetermined data sets and the data set for the unknown character usingcross correlation; identifying the maximum correlation value; andidentifying the unknown character as the MICR character corresponding tothe data set having the maximum correlation value.
 16. The method ofclaim 15 wherein the step of determining a correlation value using crosscorrelation comprises the step of determining the sum of the crossproduct between each of the predetermined data sets and the data set forthe unknown character.
 17. The method of claim 15 comprising the step ofdetermining a normalization value for the cross correlation of each dataset and the data set of the unknown character and dividing thecorrelation value by the normalization value.
 18. The method of claim 15wherein the step of scanning comprises conveying the document past themagnetic read head and measuring the magnetic field strength as thedocument passes the read head.
 19. The method of claim 15 comprises thestep of an alternative comparison, comprising the steps of: providing aplurality of alternate data sets corresponding to the MICR characters;determining a correlation value of the similarity between each of thealternate data sets and the data set for the unknown character usingcross correlation; identifying the maximum correlation value of both thepredetermined data sets and the alternate data sets; and identifying theunknown character as the MICR character corresponding to the data sethaving the maximum correlation value.
 20. The method of claim 19comprising the step of determining whether the maximum correlation valueexceeds a threshold and the alternate comparison is performed if thecorrelation value does not exceed the threshold.
 21. The method of claim19 wherein the method comprises determining the length of time that ittakes the character to pass the read heads, and the alternate comparisonis performed if the length of time is above a time threshold.
 22. Themethod of claim 19 wherein the method comprises determining the lengthof time that it takes the character to pass the read heads, and thealternate comparison is performed if the length of time is below a timethreshold.
 23. The method of claim 15 comprising the steps of scanningthe document to obtain a set of optical image data corresponding to thedocument, and exporting the optical image data and data regarding theidentified MICR character to a data file for the document.
 24. Themethod of claim 15 comprising the steps of determining the orientationof the document and selectively re-orienting the document.
 25. Themethod of claim 24 comprising the step of sorting the document inresponse to a detected characteristic of the document.
 26. The method ofclaim 15 wherein each cross correlation in the step of determining acorrelation value comprises cross correlating the data set for theunknown character and one of the predetermined data sets by determiningthe product of each data point in the data set for the unknown characterwith the corresponding data point in the one predetermined data set andsumming the products.
 27. The method of claim 15 comprising the stepsof: incrementally shifting the data to re-order the data for the unknownMICR character to provide a plurality of re-ordered data sets for theunknown MICR character; determining a correlation value of thesimilarity between each of the predetermined data sets and each of there-ordered data sets for the unknown character using cross correlation.28. The method of claim 15 comprising the steps of: incrementallyshifting the data to re-order the data for each of the predetermineddata sets to provide a plurality of re-ordered data sets for each of thepredetermined data sets; determining a correlation value of thesimilarity between each of the re-ordered predetermined data sets andthe data set for the unknown character using cross correlation.
 29. Amethod for identifying an unknown marking on a document, comprising thesteps of: providing a document having an unknown marking printed withmagnetic ink; magnetizing the unknown marking; scanning the document toobtain a set of data indicative of the magnetized marking; providing aplurality of predetermined data sets wherein each data set correspondsto a known marking; determining a correlation value of the similaritybetween each of the predetermined data sets and the data set for theunknown marking, wherein each correlation value is based on thesummation of the cross product of the data set for the unknown markingand one of the predetermined data sets; incrementally shifting the datato re-order the data for the unknown marking to provide a plurality ofre-ordered data sets for the unknown marking; determining a correlationvalue of the similarity between each of the predetermined data sets andeach of the re-ordered data sets for the unknown marking using crosscorrelation; and identifying the unknown marking as the markingcorresponding to the data set having the maximum correlation value. 30.The method of claim 29 comprising the steps of determining an offsetvalue for each data set based upon the corresponding mean value of eachdata set, and offsetting each data point in each data set by thecorresponding offset value.
 31. The method of claim 29 comprising thestep of determining a normalization value for the cross correlation ofeach data set and the data set of the unknown marking and dividing thecorrelation value by the normalization value.
 32. The method of claim 29wherein the step of scanning comprises conveying the document past amagnetic read head and measuring the magnetic field strength as thedocument passes the read head.
 33. The method of claim 29 comprises thestep of an alternative comparison, comprising the steps of: providing aplurality of alternate data sets corresponding to the known markings;determining a correlation value of the similarity between each of thealternate data sets and the data set for the unknown marking using crosscorrelation; identifying the maximum correlation value of both thepredetermined data sets and the alternate data sets; and identifying theunknown marking as the marking corresponding to the data set having themaximum correlation value.
 34. The method of claim 33 comprising thestep of determining whether the maximum correlation value exceeds athreshold and the alternate comparison is performed if the correlationvalue does not exceed the threshold.
 35. The method of claim 33 whereinthe method comprises determining the length of time that it takes thecharacter to scan the marking, and the alternate comparison is performedif the length of time is above a time threshold.
 36. The method of claim33 wherein the method comprises determining the length of time that ittakes the character to scan the marking, and the alternate comparison isperformed if the length of time is below a time threshold.
 37. Themethod of claim 29 comprising the steps of scanning the document toobtain a set of optical image data corresponding to the document, andexporting the optical image data and data regarding the identifiedmarking to a data file for the document.
 38. The method of claim 29comprising the steps of determining the orientation of the document andselectively manipulating either the data set for the unknown marking orthe data sets for the known markings.
 39. The method of claim 38comprising the step of sorting the document in response to a detectedcharacteristic of the document.
 40. A method for identifying an unknownmarking on a document, comprising the steps of: providing a documenthaving an unknown marking printed with magnetic ink; magnetizing theunknown marking; scanning the document to obtain a set of dataindicative of the magnetized marking; providing a plurality ofpredetermined data sets wherein each data set corresponds to a knownmarking; determining a correlation value of the similarity between eachof the predetermined data sets and the data set for the unknown marking,wherein each correlation value is based on the summation of the crossproduct of the data set for the unknown marking and one of thepredetermined data sets; incrementally shifting the data to re-order thedata for each of the predetermined data sets to provide a plurality ofre-ordered data sets for each of the predetermined data sets;determining a correlation value of the similarity between each of there-ordered predetermined data sets and the data set for the unknowncharacter using cross correlation; and identifying the unknown markingas the marking corresponding to the data set having the maximumcorrelation value.
 41. The method of claim 40 comprising the steps ofdetermining an offset value for each data set based upon thecorresponding mean value of each data set, and offsetting each datapoint in each data set by the corresponding offset value.
 42. The methodof claim 40 comprising the step of determining a normalization value forthe cross correlation of each data set and the data set of the unknownmarking and dividing the correlation value by the normalization value.43. The method of claim 40 wherein the step of scanning comprisesconveying the document past a magnetic read head and measuring themagnetic field strength as the document passes the read head.
 44. Themethod of claim 40 comprises the step of an alternative comparison,comprising the steps of: providing a plurality of alternate data setscorresponding to the known markings; determining a correlation value ofthe similarity between each of the alternate data sets and the data setfor the unknown marking using cross correlation; identifying the maximumcorrelation value of both the predetermined data sets and the alternatedata sets; and identifying the unknown marking as the markingcorresponding to the data set having the maximum correlation value. 45.The method of claim 44 comprising the step of determining whether themaximum correlation value exceeds a threshold and the alternatecomparison is performed if the correlation value does not exceed thethreshold.
 46. The method of claim 44 wherein the method comprisesdetermining the length of time that it takes the character to scan themarking, and the alternate comparison is performed if the length of timeis above a time threshold.
 47. The method of claim 44 wherein the methodcomprises determining the length of time that it takes the character toscan the marking, and the alternate comparison is performed if thelength of time is below a time threshold.
 48. The method of claim 40comprising the steps of scanning the document to obtain a set of opticalimage data corresponding to the document, and exporting the opticalimage data and data regarding the identified marking to a data file forthe document.
 49. The method of claim 40 comprising the steps ofdetermining the orientation of the document and selectively manipulatingeither the data set for the unknown marking or the data sets for theknown markings.
 50. A method for identifying an unknown MICR characteron a document, comprising the steps of: providing a document having anunknown MICR character printed with magnetic ink; imparting a magneticcharge onto the magnetic ink to magnetize the unknown character;scanning the unknown character with a magnetic read head to obtain a setof data indicative of the unknown character; providing a plurality ofpredetermined data sets wherein each data set corresponds to aparticular MICR character; determining a correlation value of thesimilarity between each of the predetermined data sets and the data setfor the unknown character using cross correlation; providing a pluralityof alternate data sets corresponding to the MICR characters; determininga correlation value of the similarity between each of the alternate datasets and the data set for the unknown character using cross correlation;identifying the maximum correlation value of both the predetermined datasets and the alternate data sets; and identifying the unknown characteras the MICR character corresponding to the data set having the maximumcorrelation value.
 51. The method of claim 50 wherein the step ofdetermining a correlation value using cross correlation comprises thestep of determining the sum of the cross product between each of thepredetermined data sets and the data set for the unknown character. 52.The method of claim 50 wherein the step of scanning comprises conveyingthe document past the magnetic read head and measuring the magneticfield strength as the document passes the read head.
 53. The method ofclaim 50 comprising the step of determining whether the maximumcorrelation value exceeds a threshold and the alternate comparison isperformed if the correlation value does not exceed the threshold. 54.The method of claim 50 wherein the method comprises determining thelength of time that it takes the character to pass the read heads, andthe alternate comparison is performed if the length of time is above atime threshold.
 55. The method of claim 50 wherein the method comprisesdetermining the length of time that it takes the character to pass theread heads, and the alternate comparison is performed if the length oftime is below a time threshold.
 56. The method of claim 50 comprisingthe steps of scanning the document to obtain a set of optical image datacorresponding to the document, and exporting the optical image data anddata regarding the identified MICR character to a data file for thedocument.
 57. The method of claim 50 comprising the steps of determiningthe orientation of the document and selectively re-orienting thedocument.
 58. The method of claim 50 wherein each cross correlation inthe step of determining a correlation value comprises cross correlatingthe data set for the unknown character and one of the predetermined datasets by determining the product of each data point in the data set forthe unknown character with the corresponding data point in the onepredetermined data set and summing the products.